[CVPR'22] Official PyTorch implementation of Integrative Few-Shot Learning for Classification and Segmentation

Fusing-and-Filling GAN (F2GAN) for few-shot image generation, ACM MM2020

[ECCV2022] PyTorch re-implementation of Self-Supervision Can Be a Good Few-Shot Learner

Codes for the experiments in our EMNLP 2021 paper "Open Aspect Target Sentiment Classification with Natural Language Prompts"

(NeurIPS 2021) Realistic Evaluation of Transductive Few-Shot Learning

Black-box Few-shot Knowledge Distillation

bullet: A Zero-Shot / Few-Shot Learning, LLM Based, text classification framework

[ICASSP-2022] Visual Representation Learning with Self-Supervised Attention for Low-Label High-Data Regime

Few-shot satellite image classification for bringing deep learning on board OPS-SAT

CNN based Siamese network for similarity detection. A toy database is used to demonstrate its functionality.

The code for "Efficient-PrototypicalNet with self knowledge distillation for few-shot learning"

A lightweight PyTorch implementation of Prototypical Networks using a ViT-Small/16 backbone for few-shot classification on standard benchmarks (Mini-ImageNet, CUB-200, CIFAR-FS, FC100). Includes both production scripts for training & evaluation and Jupyter notebooks for interactive analysis.

Evaluation of PromptAD’s robustness under various image corruptions for few-shot anomaly detection.

Lightweight PyTorch implementation of Prototypical Networks with ViT-Small/16 for few-shot learning. Achieve top results on CIFAR-FS and FC100. 🌟📊